{
  "schemaVersion": "1.0",
  "item": {
    "slug": "crypto-self-learning",
    "name": "Crypto Self-Learning",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
    "sourceUrl": "https://clawhub.ai/totaleasy/crypto-self-learning",
    "canonicalUrl": "https://clawhub.ai/totaleasy/crypto-self-learning",
    "targetPlatform": "OpenClaw"
  },
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    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "SKILL.md",
      "data/trades.json",
      "scripts/analyze.py",
      "scripts/generate_rules.py",
      "scripts/log_trade.py",
      "scripts/update_memory.py"
    ],
    "primaryDoc": "SKILL.md",
    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
        }
      ]
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      "reason": "direct_download_ok",
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      "checkedAt": "2026-04-30T16:55:25.780Z",
      "expiresAt": "2026-05-07T16:55:25.780Z",
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        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/crypto-self-learning"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/crypto-self-learning",
    "agentPageUrl": "https://openagent3.xyz/skills/crypto-self-learning/agent",
    "manifestUrl": "https://openagent3.xyz/skills/crypto-self-learning/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/crypto-self-learning/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Crypto Self-Learning 🧠",
        "body": "AI-powered self-improvement system for crypto trading. Learn from every trade to increase accuracy over time."
      },
      {
        "title": "🎯 Core Concept",
        "body": "Every trade is a lesson. This skill:\n\nLogs every trade with full context\nAnalyzes patterns in wins vs losses\nGenerates rules from real data\nUpdates memory automatically"
      },
      {
        "title": "📝 Log a Trade",
        "body": "After EVERY trade (win or loss), log it:\n\npython3 {baseDir}/scripts/log_trade.py \\\n  --symbol BTCUSDT \\\n  --direction LONG \\\n  --entry 78000 \\\n  --exit 79500 \\\n  --pnl_percent 1.92 \\\n  --leverage 5 \\\n  --reason \"RSI oversold + support bounce\" \\\n  --indicators '{\"rsi\": 28, \"macd\": \"bullish_cross\", \"ma_position\": \"above_50\"}' \\\n  --market_context '{\"btc_trend\": \"up\", \"dxy\": 104.5, \"russell\": \"up\", \"day\": \"tuesday\", \"hour\": 14}' \\\n  --result WIN \\\n  --notes \"Clean setup, followed the plan\""
      },
      {
        "title": "Required Fields:",
        "body": "FieldDescriptionExample--symbolTrading pairBTCUSDT--directionLONG or SHORTLONG--entryEntry price78000--exitExit price79500--pnl_percentProfit/Loss %1.92 or -2.5--resultWIN or LOSSWIN"
      },
      {
        "title": "Optional but Recommended:",
        "body": "FieldDescription--leverageLeverage used--reasonWhy you entered--indicatorsJSON with indicators at entry--market_contextJSON with macro conditions--notesPost-trade observations"
      },
      {
        "title": "📊 Analyze Performance",
        "body": "Run analysis to discover patterns:\n\npython3 {baseDir}/scripts/analyze.py\n\nOutputs:\n\nWin rate by direction (LONG vs SHORT)\nWin rate by day of week\nWin rate by RSI ranges\nWin rate by leverage\nBest/worst setups identified\nSuggested rules"
      },
      {
        "title": "Analyze Specific Filters:",
        "body": "python3 {baseDir}/scripts/analyze.py --symbol BTCUSDT\npython3 {baseDir}/scripts/analyze.py --direction LONG\npython3 {baseDir}/scripts/analyze.py --min-trades 10"
      },
      {
        "title": "🧠 Generate Rules",
        "body": "Extract actionable rules from your trade history:\n\npython3 {baseDir}/scripts/generate_rules.py\n\nThis analyzes patterns and outputs rules like:\n\n🚫 AVOID: LONG when RSI > 70 (win rate: 23%, n=13)\n✅ PREFER: SHORT on Mondays (win rate: 78%, n=9)\n⚠️ CAUTION: Trades with leverage > 10x (win rate: 35%, n=20)"
      },
      {
        "title": "📈 Auto-Update Memory",
        "body": "Apply learned rules to agent memory:\n\npython3 {baseDir}/scripts/update_memory.py --memory-path /path/to/MEMORY.md\n\nThis appends a \"## 🧠 Learned Rules\" section with data-driven insights."
      },
      {
        "title": "Dry Run (preview changes):",
        "body": "python3 {baseDir}/scripts/update_memory.py --memory-path /path/to/MEMORY.md --dry-run"
      },
      {
        "title": "📋 View Trade History",
        "body": "python3 {baseDir}/scripts/log_trade.py --list\npython3 {baseDir}/scripts/log_trade.py --list --last 10\npython3 {baseDir}/scripts/log_trade.py --stats"
      },
      {
        "title": "🔄 Weekly Review",
        "body": "Run weekly to see progress:\n\npython3 {baseDir}/scripts/weekly_review.py\n\nGenerates:\n\nThis week's performance vs last week\nNew patterns discovered\nRules that worked/failed\nRecommendations for next week"
      },
      {
        "title": "📁 Data Storage",
        "body": "Trades are stored in {baseDir}/data/trades.json:\n\n{\n  \"trades\": [\n    {\n      \"id\": \"uuid\",\n      \"timestamp\": \"2026-02-02T13:00:00Z\",\n      \"symbol\": \"BTCUSDT\",\n      \"direction\": \"LONG\",\n      \"entry\": 78000,\n      \"exit\": 79500,\n      \"pnl_percent\": 1.92,\n      \"result\": \"WIN\",\n      \"indicators\": {...},\n      \"market_context\": {...}\n    }\n  ]\n}"
      },
      {
        "title": "🎯 Best Practices",
        "body": "Log EVERY trade - Wins AND losses\nBe honest - Don't skip bad trades\nAdd context - More data = better patterns\nReview weekly - Patterns emerge over time\nTrust the data - If data says avoid something, AVOID IT"
      },
      {
        "title": "🔗 Integration with tess-cripto",
        "body": "Add to tess-cripto's workflow:\n\nBefore trade: Check rules in MEMORY.md\nAfter trade: Log with full context\nWeekly: Run analysis and update memory\n\nSkill by Total Easy Software - Learn from every trade 🧠📈"
      }
    ],
    "body": "Crypto Self-Learning 🧠\n\nAI-powered self-improvement system for crypto trading. Learn from every trade to increase accuracy over time.\n\n🎯 Core Concept\n\nEvery trade is a lesson. This skill:\n\nLogs every trade with full context\nAnalyzes patterns in wins vs losses\nGenerates rules from real data\nUpdates memory automatically\n📝 Log a Trade\n\nAfter EVERY trade (win or loss), log it:\n\npython3 {baseDir}/scripts/log_trade.py \\\n  --symbol BTCUSDT \\\n  --direction LONG \\\n  --entry 78000 \\\n  --exit 79500 \\\n  --pnl_percent 1.92 \\\n  --leverage 5 \\\n  --reason \"RSI oversold + support bounce\" \\\n  --indicators '{\"rsi\": 28, \"macd\": \"bullish_cross\", \"ma_position\": \"above_50\"}' \\\n  --market_context '{\"btc_trend\": \"up\", \"dxy\": 104.5, \"russell\": \"up\", \"day\": \"tuesday\", \"hour\": 14}' \\\n  --result WIN \\\n  --notes \"Clean setup, followed the plan\"\n\nRequired Fields:\nField\tDescription\tExample\n--symbol\tTrading pair\tBTCUSDT\n--direction\tLONG or SHORT\tLONG\n--entry\tEntry price\t78000\n--exit\tExit price\t79500\n--pnl_percent\tProfit/Loss %\t1.92 or -2.5\n--result\tWIN or LOSS\tWIN\nOptional but Recommended:\nField\tDescription\n--leverage\tLeverage used\n--reason\tWhy you entered\n--indicators\tJSON with indicators at entry\n--market_context\tJSON with macro conditions\n--notes\tPost-trade observations\n📊 Analyze Performance\n\nRun analysis to discover patterns:\n\npython3 {baseDir}/scripts/analyze.py\n\n\nOutputs:\n\nWin rate by direction (LONG vs SHORT)\nWin rate by day of week\nWin rate by RSI ranges\nWin rate by leverage\nBest/worst setups identified\nSuggested rules\nAnalyze Specific Filters:\npython3 {baseDir}/scripts/analyze.py --symbol BTCUSDT\npython3 {baseDir}/scripts/analyze.py --direction LONG\npython3 {baseDir}/scripts/analyze.py --min-trades 10\n\n🧠 Generate Rules\n\nExtract actionable rules from your trade history:\n\npython3 {baseDir}/scripts/generate_rules.py\n\n\nThis analyzes patterns and outputs rules like:\n\n🚫 AVOID: LONG when RSI > 70 (win rate: 23%, n=13)\n✅ PREFER: SHORT on Mondays (win rate: 78%, n=9)\n⚠️ CAUTION: Trades with leverage > 10x (win rate: 35%, n=20)\n\n📈 Auto-Update Memory\n\nApply learned rules to agent memory:\n\npython3 {baseDir}/scripts/update_memory.py --memory-path /path/to/MEMORY.md\n\n\nThis appends a \"## 🧠 Learned Rules\" section with data-driven insights.\n\nDry Run (preview changes):\npython3 {baseDir}/scripts/update_memory.py --memory-path /path/to/MEMORY.md --dry-run\n\n📋 View Trade History\npython3 {baseDir}/scripts/log_trade.py --list\npython3 {baseDir}/scripts/log_trade.py --list --last 10\npython3 {baseDir}/scripts/log_trade.py --stats\n\n🔄 Weekly Review\n\nRun weekly to see progress:\n\npython3 {baseDir}/scripts/weekly_review.py\n\n\nGenerates:\n\nThis week's performance vs last week\nNew patterns discovered\nRules that worked/failed\nRecommendations for next week\n📁 Data Storage\n\nTrades are stored in {baseDir}/data/trades.json:\n\n{\n  \"trades\": [\n    {\n      \"id\": \"uuid\",\n      \"timestamp\": \"2026-02-02T13:00:00Z\",\n      \"symbol\": \"BTCUSDT\",\n      \"direction\": \"LONG\",\n      \"entry\": 78000,\n      \"exit\": 79500,\n      \"pnl_percent\": 1.92,\n      \"result\": \"WIN\",\n      \"indicators\": {...},\n      \"market_context\": {...}\n    }\n  ]\n}\n\n🎯 Best Practices\nLog EVERY trade - Wins AND losses\nBe honest - Don't skip bad trades\nAdd context - More data = better patterns\nReview weekly - Patterns emerge over time\nTrust the data - If data says avoid something, AVOID IT\n🔗 Integration with tess-cripto\n\nAdd to tess-cripto's workflow:\n\nBefore trade: Check rules in MEMORY.md\nAfter trade: Log with full context\nWeekly: Run analysis and update memory\n\nSkill by Total Easy Software - Learn from every trade 🧠📈"
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/totaleasy/crypto-self-learning",
    "publisherUrl": "https://clawhub.ai/totaleasy/crypto-self-learning",
    "owner": "totaleasy",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/crypto-self-learning",
    "downloadUrl": "https://openagent3.xyz/downloads/crypto-self-learning",
    "agentUrl": "https://openagent3.xyz/skills/crypto-self-learning/agent",
    "manifestUrl": "https://openagent3.xyz/skills/crypto-self-learning/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/crypto-self-learning/agent.md"
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}